The above-mentioned challenges have promoted the development of the deepfake detection technology. At present, most detection algorithms [1]}, [2]}, [3]}, [4]}, [5]}, [6]} treat it as a two-class classification problem, using data-driven training of deep convolutional networks to detect fake faces. Although these methods achieve good detection results, the models are lacking in robustness and generalization. When the test
images are subject to some common transformations (like resizing, blurring), their performances are significantly reduced.
